scholarly journals Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity

2013 ◽  
Vol 10 (1) ◽  
pp. 103 ◽  
Author(s):  
Pippa Griew ◽  
Melvyn Hillsdon ◽  
Charlie Foster ◽  
Emma Coombes ◽  
Andy Jones ◽  
...  
Keyword(s):  
Physical Activity ◽  
Audit Tool ◽  
Google Street View ◽  
Street View

Adaptation and testing of a microscale audit tool to assess liveability using google street view: MAPS-liveability

2021 ◽  
Vol 22 ◽  
pp. 101226
Author(s):  
Claire L. Cleland ◽  
Sara Ferguson ◽  
Frank Kee ◽  
Paul Kelly ◽  
Andrew James Williams ◽  
...  
Keyword(s):  
Audit Tool ◽  
Google Street View ◽  
Street View

scholarly journals Development and Validation of a Google Street View Pedestrian Safety Audit Tool

Epidemiology ◽  
2020 ◽  
Vol 31 (2) ◽  
pp. 301-309 ◽  
Author(s):  
Stephen J. Mooney ◽  
Katherine Wheeler-Martin ◽  
Laura M. Fiedler ◽  
Celine M. LaBelle ◽  
Taylor Lampe ◽  
...  
Keyword(s):  
Pedestrian Safety ◽  
Audit Tool ◽  
Google Street View ◽  
Street View ◽  
Development And Validation

scholarly journals Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

2020 ◽  
Vol 17 (10) ◽  
pp. 3659
Author(s):  
Lynn Phan ◽  
Weijun Yu ◽  
Jessica M. Keralis ◽  
Krishay Mukhija ◽  
Pallavi Dwivedi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Health Outcomes ◽  
State Level ◽  
Premature Mortality ◽  
The United States ◽  
Single Family ◽  
Google Street View ◽  
Street View ◽  
Neighborhood Built Environment

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

scholarly journals Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jesse Whitehead ◽  
Melody Smith ◽  
Yvonne Anderson ◽  
Yijun Zhang ◽  
Stephanie Wu ◽  
...  
Keyword(s):  
Physical Activity ◽  
Spatial Data ◽  
Practical Approach ◽  
Google Street View ◽  
Environmental Features ◽  
Temporal Specificity ◽  
Aotearoa New Zealand ◽  
Street View ◽  
District Council ◽  
Geospatial Datasets

Abstract Background Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand. Methods We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets. Results A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated. Conclusions Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.

scholarly journals Comparing the Normalized Difference Vegetation Index with the Google Street View Measure of Vegetation to Assess Associations between Greenness, Walkability, Recreational Physical Activity, and Health in Ottawa, Canada

2018 ◽  
Vol 15 (8) ◽  
pp. 1719 ◽  
Author(s):  
Paul J. Villeneuve ◽  
Renate L. Ysseldyk ◽  
Ariel Root ◽  
Sarah Ambrose ◽  
Jason DiMuzio ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Physical Activities ◽  
Living Environment ◽  
Active Living ◽  
Recreational Physical Activity ◽  
Recreational Activities ◽  
Google Street View ◽  
Street View

The manner in which features of the built environment, such as walkability and greenness, impact participation in recreational activities and health are complex. We analyzed survey data provided by 282 Ottawa adults in 2016. The survey collected information on participation in recreational physical activities by season, and whether these activities were performed within participants’ neighbourhoods. The SF-12 instrument was used to characterize their overall mental and physical health. Measures of active living environment, and the satellite derived Normalized Difference Vegetation Index (NDVI) and Google Street View (GSV) greenness indices were assigned to participants’ residential addresses. Logistic regression and least squares regression were used to characterize associations between these measures and recreational physical activity, and self-reported health. The NDVI was not associated with participation in recreational activities in either the winter or summer, or physical or mental health. In contrast, the GSV was positively associated with participation in recreational activities during the summer. Specifically, those in the highest quartile spent, on average, 5.4 more hours weekly on recreational physical activities relative to those in the lowest quartile (p = 0.01). Active living environments were associated with increased utilitarian walking, and reduced reliance on use of motor vehicles. Our findings provide support for the hypothesis that neighbourhood greenness may play an important role in promoting participation in recreational physical activity during the summer.

Deep learning-based visual crack detection using Google Street View images

2021 ◽  
Author(s):  
Mohsen Maniat ◽  
Charles V. Camp ◽  
Ali R. Kashani
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Google Street View ◽  
Street View

scholarly journals Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ervin Yohannes ◽  
Chih-Yang Lin ◽  
Timothy K. Shih ◽  
Chen-Ya Hong ◽  
Avirmed Enkhbat ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Attention Network ◽  
Google Street View ◽  
Street View ◽  
Detection And Recognition

scholarly journals STATISTICAL ANALYSIS VIA GOOGLE STREET VIEW OF SHOP FACADE DESIGN

2012 ◽  
Vol 18 (39) ◽  
pp. 693-698
Author(s):  
Eisuke TABATA ◽  
Kazemitsu FUKAMATSU ◽  
Kazuhisa TSUNEKAWA ◽  
Gen TANIGUCHI
Keyword(s):  
Statistical Analysis ◽  
Google Street View ◽  
Street View
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